Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform Mass Spectrometry Data
Abstract
The fuzzy logic method is applied to classification of mass spectra obtained with an imaging internal source Fourier transform mass spectrometer (I2LD-FTMS). Traditionally, an operator uses the relative abundance of ions with specific mass-to-charge (m/z) ratios to categorize spectra. An operator does this by comparing the spectrum of m/z versus abundance of an unknown sample against a library of spectra from known samples. Automated positioning and acquisition allow the I2LD-FTMS to acquire data from very large grids, which would require classification of up to 3600 spectra per hour to keep pace with the acquisition. The tedious job of classifying numerous spectra generated in an I2LD-FTMS imaging application can be replaced by a fuzzy rule base if the cues an operator uses can be encapsulated. Appropriate methods for assigning fuzzy membership values for inputs (e.g., mass spectrum abundances) and choice of fuzzy inference operators to translate linguistic antecedent into confidence values for the consequence (or in this case the classification) is followed by using the maximum confidence and a necessary minimum threshold for making a crisp decision. This paper also describes a method for gathering statistics on ions, which are not currently used in the rule base, but which may bemore »
- Authors:
- Publication Date:
- Research Org.:
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 939434
- Report Number(s):
- INL/JOU-07-12206
TRN: US200823%%184
- DOE Contract Number:
- DE-AC07-99ID-13727
- Resource Type:
- Journal Article
- Journal Name:
- Journal of the Idaho Academy of Science
- Additional Journal Information:
- Journal Volume: 44; Journal Issue: 1
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; 58 GEOSCIENCES; ABUNDANCE; CLASSIFICATION; DESORPTION; FUZZY LOGIC; LASERS; MASS SPECTRA; MASS SPECTROMETERS; MASS SPECTROSCOPY; POSITIONING; SPECTRA; STATISTICS; Automation; Basalt; chemical imaging; Classification; Fourier Transform Mass Spectrometry; Fuzzy Logic; mapping; Minerals
Citation Formats
McJunkin, Timothy R, and Scott, Jill R. Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform Mass Spectrometry Data. United States: N. p., 2008.
Web.
McJunkin, Timothy R, & Scott, Jill R. Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform Mass Spectrometry Data. United States.
McJunkin, Timothy R, and Scott, Jill R. 2008.
"Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform Mass Spectrometry Data". United States.
@article{osti_939434,
title = {Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform Mass Spectrometry Data},
author = {McJunkin, Timothy R and Scott, Jill R},
abstractNote = {The fuzzy logic method is applied to classification of mass spectra obtained with an imaging internal source Fourier transform mass spectrometer (I2LD-FTMS). Traditionally, an operator uses the relative abundance of ions with specific mass-to-charge (m/z) ratios to categorize spectra. An operator does this by comparing the spectrum of m/z versus abundance of an unknown sample against a library of spectra from known samples. Automated positioning and acquisition allow the I2LD-FTMS to acquire data from very large grids, which would require classification of up to 3600 spectra per hour to keep pace with the acquisition. The tedious job of classifying numerous spectra generated in an I2LD-FTMS imaging application can be replaced by a fuzzy rule base if the cues an operator uses can be encapsulated. Appropriate methods for assigning fuzzy membership values for inputs (e.g., mass spectrum abundances) and choice of fuzzy inference operators to translate linguistic antecedent into confidence values for the consequence (or in this case the classification) is followed by using the maximum confidence and a necessary minimum threshold for making a crisp decision. This paper also describes a method for gathering statistics on ions, which are not currently used in the rule base, but which may be candidates for making the rule base more accurate and complete or to form new rule bases based on data obtained from known samples. A spatial method for classifying spectra with low membership values, based on neighboring sample classifications, is also presented.},
doi = {},
url = {https://www.osti.gov/biblio/939434},
journal = {Journal of the Idaho Academy of Science},
number = 1,
volume = 44,
place = {United States},
year = {Sun Jun 01 00:00:00 EDT 2008},
month = {Sun Jun 01 00:00:00 EDT 2008}
}